5de98f2d50
Summary: sounds like we're willing to tradeoff minor inaccuracy in stats for speed. start with histogram stats. ticker stats will be harder (and, IMO, we shouldn't change them in this manner) as many test cases rely on them being exactly correct. Closes https://github.com/facebook/rocksdb/pull/2720 Differential Revision: D5607884 Pulled By: ajkr fbshipit-source-id: 1b754cda35ea6b252d1fdd5aa3cfb58866506372
303 lines
10 KiB
C++
303 lines
10 KiB
C++
// Copyright (c) 2011-present, Facebook, Inc. All rights reserved.
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// This source code is licensed under both the GPLv2 (found in the
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// COPYING file in the root directory) and Apache 2.0 License
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// (found in the LICENSE.Apache file in the root directory).
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//
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// Copyright (c) 2011 The LevelDB Authors. All rights reserved.
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// Use of this source code is governed by a BSD-style license that can be
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// found in the LICENSE file. See the AUTHORS file for names of contributors.
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#ifndef __STDC_FORMAT_MACROS
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#define __STDC_FORMAT_MACROS
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#endif
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#include "monitoring/histogram.h"
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#include <inttypes.h>
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#include <cassert>
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#include <math.h>
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#include <stdio.h>
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#include "port/port.h"
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#include "util/cast_util.h"
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namespace rocksdb {
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HistogramBucketMapper::HistogramBucketMapper()
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:
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// Add newer bucket index here.
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// Should be always added in sorted order.
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// If you change this, you also need to change
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// size of array buckets_ in HistogramImpl
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bucketValues_(
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{1, 2, 3, 4, 5, 6,
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7, 8, 9, 10, 12, 14,
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16, 18, 20, 25, 30, 35,
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40, 45, 50, 60, 70, 80,
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90, 100, 120, 140, 160, 180,
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200, 250, 300, 350, 400, 450,
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500, 600, 700, 800, 900, 1000,
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1200, 1400, 1600, 1800, 2000, 2500,
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3000, 3500, 4000, 4500, 5000, 6000,
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7000, 8000, 9000, 10000, 12000, 14000,
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16000, 18000, 20000, 25000, 30000, 35000,
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40000, 45000, 50000, 60000, 70000, 80000,
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90000, 100000, 120000, 140000, 160000, 180000,
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200000, 250000, 300000, 350000, 400000, 450000,
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500000, 600000, 700000, 800000, 900000, 1000000,
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1200000, 1400000, 1600000, 1800000, 2000000, 2500000,
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3000000, 3500000, 4000000, 4500000, 5000000, 6000000,
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7000000, 8000000, 9000000, 10000000, 12000000, 14000000,
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16000000, 18000000, 20000000, 25000000, 30000000, 35000000,
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40000000, 45000000, 50000000, 60000000, 70000000, 80000000,
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90000000, 100000000, 120000000, 140000000, 160000000, 180000000,
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200000000, 250000000, 300000000, 350000000, 400000000, 450000000,
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500000000, 600000000, 700000000, 800000000, 900000000, 1000000000}),
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maxBucketValue_(bucketValues_.back()),
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minBucketValue_(bucketValues_.front()) {
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for (size_t i =0; i < bucketValues_.size(); ++i) {
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valueIndexMap_[bucketValues_[i]] = i;
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}
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}
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size_t HistogramBucketMapper::IndexForValue(const uint64_t value) const {
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if (value >= maxBucketValue_) {
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return bucketValues_.size() - 1;
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} else if ( value >= minBucketValue_ ) {
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std::map<uint64_t, uint64_t>::const_iterator lowerBound =
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valueIndexMap_.lower_bound(value);
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if (lowerBound != valueIndexMap_.end()) {
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return static_cast<size_t>(lowerBound->second);
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} else {
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return 0;
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}
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} else {
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return 0;
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}
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}
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namespace {
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const HistogramBucketMapper bucketMapper;
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}
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HistogramStat::HistogramStat()
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: num_buckets_(bucketMapper.BucketCount()) {
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assert(num_buckets_ == sizeof(buckets_) / sizeof(*buckets_));
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Clear();
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}
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void HistogramStat::Clear() {
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min_.store(bucketMapper.LastValue(), std::memory_order_relaxed);
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max_.store(0, std::memory_order_relaxed);
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num_.store(0, std::memory_order_relaxed);
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sum_.store(0, std::memory_order_relaxed);
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sum_squares_.store(0, std::memory_order_relaxed);
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for (unsigned int b = 0; b < num_buckets_; b++) {
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buckets_[b].store(0, std::memory_order_relaxed);
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}
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};
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bool HistogramStat::Empty() const { return num() == 0; }
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void HistogramStat::Add(uint64_t value) {
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// This function is designed to be lock free, as it's in the critical path
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// of any operation. Each individual value is atomic and the order of updates
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// by concurrent threads is tolerable.
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const size_t index = bucketMapper.IndexForValue(value);
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assert(index < num_buckets_);
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buckets_[index].store(buckets_[index].load(std::memory_order_relaxed) + 1,
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std::memory_order_relaxed);
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uint64_t old_min = min();
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if (value < old_min) {
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min_.store(value, std::memory_order_relaxed);
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}
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uint64_t old_max = max();
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if (value > old_max) {
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max_.store(value, std::memory_order_relaxed);
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}
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num_.store(num_.load(std::memory_order_relaxed) + 1,
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std::memory_order_relaxed);
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sum_.store(sum_.load(std::memory_order_relaxed) + value,
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std::memory_order_relaxed);
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sum_squares_.store(
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sum_squares_.load(std::memory_order_relaxed) + value * value,
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std::memory_order_relaxed);
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}
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void HistogramStat::Merge(const HistogramStat& other) {
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// This function needs to be performned with the outer lock acquired
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// However, atomic operation on every member is still need, since Add()
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// requires no lock and value update can still happen concurrently
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uint64_t old_min = min();
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uint64_t other_min = other.min();
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while (other_min < old_min &&
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!min_.compare_exchange_weak(old_min, other_min)) {}
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uint64_t old_max = max();
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uint64_t other_max = other.max();
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while (other_max > old_max &&
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!max_.compare_exchange_weak(old_max, other_max)) {}
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num_.fetch_add(other.num(), std::memory_order_relaxed);
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sum_.fetch_add(other.sum(), std::memory_order_relaxed);
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sum_squares_.fetch_add(other.sum_squares(), std::memory_order_relaxed);
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for (unsigned int b = 0; b < num_buckets_; b++) {
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buckets_[b].fetch_add(other.bucket_at(b), std::memory_order_relaxed);
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}
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}
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double HistogramStat::Median() const {
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return Percentile(50.0);
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}
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double HistogramStat::Percentile(double p) const {
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double threshold = num() * (p / 100.0);
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uint64_t cumulative_sum = 0;
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for (unsigned int b = 0; b < num_buckets_; b++) {
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uint64_t bucket_value = bucket_at(b);
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cumulative_sum += bucket_value;
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if (cumulative_sum >= threshold) {
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// Scale linearly within this bucket
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uint64_t left_point = (b == 0) ? 0 : bucketMapper.BucketLimit(b-1);
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uint64_t right_point = bucketMapper.BucketLimit(b);
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uint64_t left_sum = cumulative_sum - bucket_value;
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uint64_t right_sum = cumulative_sum;
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double pos = 0;
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uint64_t right_left_diff = right_sum - left_sum;
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if (right_left_diff != 0) {
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pos = (threshold - left_sum) / right_left_diff;
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}
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double r = left_point + (right_point - left_point) * pos;
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uint64_t cur_min = min();
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uint64_t cur_max = max();
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if (r < cur_min) r = static_cast<double>(cur_min);
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if (r > cur_max) r = static_cast<double>(cur_max);
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return r;
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}
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}
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return static_cast<double>(max());
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}
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double HistogramStat::Average() const {
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uint64_t cur_num = num();
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uint64_t cur_sum = sum();
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if (cur_num == 0) return 0;
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return static_cast<double>(cur_sum) / static_cast<double>(cur_num);
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}
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double HistogramStat::StandardDeviation() const {
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uint64_t cur_num = num();
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uint64_t cur_sum = sum();
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uint64_t cur_sum_squares = sum_squares();
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if (cur_num == 0) return 0;
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double variance =
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static_cast<double>(cur_sum_squares * cur_num - cur_sum * cur_sum) /
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static_cast<double>(cur_num * cur_num);
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return sqrt(variance);
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}
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std::string HistogramStat::ToString() const {
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uint64_t cur_num = num();
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std::string r;
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char buf[1650];
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snprintf(buf, sizeof(buf),
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"Count: %" PRIu64 " Average: %.4f StdDev: %.2f\n",
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cur_num, Average(), StandardDeviation());
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r.append(buf);
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snprintf(buf, sizeof(buf),
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"Min: %" PRIu64 " Median: %.4f Max: %" PRIu64 "\n",
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(cur_num == 0 ? 0 : min()), Median(), (cur_num == 0 ? 0 : max()));
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r.append(buf);
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snprintf(buf, sizeof(buf),
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"Percentiles: "
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"P50: %.2f P75: %.2f P99: %.2f P99.9: %.2f P99.99: %.2f\n",
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Percentile(50), Percentile(75), Percentile(99), Percentile(99.9),
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Percentile(99.99));
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r.append(buf);
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r.append("------------------------------------------------------\n");
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const double mult = 100.0 / cur_num;
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uint64_t cumulative_sum = 0;
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for (unsigned int b = 0; b < num_buckets_; b++) {
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uint64_t bucket_value = bucket_at(b);
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if (bucket_value <= 0.0) continue;
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cumulative_sum += bucket_value;
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snprintf(buf, sizeof(buf),
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"[ %7" PRIu64 ", %7" PRIu64 " ) %8" PRIu64 " %7.3f%% %7.3f%% ",
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(b == 0) ? 0 : bucketMapper.BucketLimit(b-1), // left
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bucketMapper.BucketLimit(b), // right
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bucket_value, // count
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(mult * bucket_value), // percentage
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(mult * cumulative_sum)); // cumulative percentage
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r.append(buf);
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// Add hash marks based on percentage; 20 marks for 100%.
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size_t marks = static_cast<size_t>(mult * bucket_value / 5 + 0.5);
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r.append(marks, '#');
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r.push_back('\n');
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}
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return r;
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}
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void HistogramStat::Data(HistogramData * const data) const {
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assert(data);
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data->median = Median();
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data->percentile95 = Percentile(95);
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data->percentile99 = Percentile(99);
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data->max = static_cast<double>(max());
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data->average = Average();
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data->standard_deviation = StandardDeviation();
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}
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void HistogramImpl::Clear() {
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std::lock_guard<std::mutex> lock(mutex_);
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stats_.Clear();
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}
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bool HistogramImpl::Empty() const {
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return stats_.Empty();
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}
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void HistogramImpl::Add(uint64_t value) {
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stats_.Add(value);
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}
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void HistogramImpl::Merge(const Histogram& other) {
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if (strcmp(Name(), other.Name()) == 0) {
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Merge(
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*static_cast_with_check<const HistogramImpl, const Histogram>(&other));
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}
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}
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void HistogramImpl::Merge(const HistogramImpl& other) {
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std::lock_guard<std::mutex> lock(mutex_);
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stats_.Merge(other.stats_);
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}
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double HistogramImpl::Median() const {
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return stats_.Median();
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}
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double HistogramImpl::Percentile(double p) const {
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return stats_.Percentile(p);
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}
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double HistogramImpl::Average() const {
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return stats_.Average();
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}
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double HistogramImpl::StandardDeviation() const {
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return stats_.StandardDeviation();
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}
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std::string HistogramImpl::ToString() const {
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return stats_.ToString();
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}
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void HistogramImpl::Data(HistogramData * const data) const {
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stats_.Data(data);
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}
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} // namespace levedb
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